{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supervised Learning Project: Finding Donors for *CharityML*\n",
    "\n",
    "This project was completed as a part of Udacity's Machine Learning Nanodegree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we will test out several supervised algorithms to accurately model individuals' income using data collected from the 1994 U.S. Census. We will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Our goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations.  Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with.  While it can be difficult to determine an individual's general income bracket directly from public sources, we can infer this value from other publically available features. \n",
    "\n",
    "The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Census+Income). The datset was donated by Ron Kohavi and Barry Becker, after being published in the article _\"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid\"_. You can find the article by Ron Kohavi [online](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf). The data we investigate here consists of small changes to the original dataset, such as removing the `'fnlwgt'` feature and records with missing or ill-formatted entries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Exploring the Data\n",
    "\n",
    "Let's start with importing the necessary libaries, reading in the data, and checking out the dataset.\n",
    "\n",
    "Note that the last column from this dataset, `'income'`, will be our target label (whether an individual makes more than, or at most, $50,000 annually). All other columns are features about each individual in the census database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   workclass education_level  education-num  marital-status  \\\n",
       "0   39   State-gov       Bachelors           13.0   Never-married   \n",
       "\n",
       "      occupation    relationship    race    sex  capital-gain  capital-loss  \\\n",
       "0   Adm-clerical   Not-in-family   White   Male        2174.0           0.0   \n",
       "\n",
       "   hours-per-week  native-country income  \n",
       "0            40.0   United-States  <=50K  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import libraries necessary for this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "\n",
    "# Import visualisation libraries\n",
    "import visuals as vs\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "from __future__ import division\n",
    "\n",
    "# Load the Census dataset\n",
    "data = pd.read_csv(\"census.csv\")\n",
    "\n",
    "# Success - Display the first record\n",
    "display(data.head(n=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple investigation of the dataset can determine how many individuals fit into either group, and tell us about the percentage of these individuals making more than \\$50,000. \n",
    "\n",
    "Let's take a look at the following: \n",
    "\n",
    "- The total number of records, `'n_records'`\n",
    "- The number of individuals making more than \\$50,000 annually, `'n_greater_50k'`.\n",
    "- The number of individuals making at most \\$50,000 annually, `'n_at_most_50k'`.\n",
    "- The percentage of individuals making more than \\$50,000 annually, `'greater_percent'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45222 entries, 0 to 45221\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   age              45222 non-null  int64  \n",
      " 1   workclass        45222 non-null  object \n",
      " 2   education_level  45222 non-null  object \n",
      " 3   education-num    45222 non-null  float64\n",
      " 4   marital-status   45222 non-null  object \n",
      " 5   occupation       45222 non-null  object \n",
      " 6   relationship     45222 non-null  object \n",
      " 7   race             45222 non-null  object \n",
      " 8   sex              45222 non-null  object \n",
      " 9   capital-gain     45222 non-null  float64\n",
      " 10  capital-loss     45222 non-null  float64\n",
      " 11  hours-per-week   45222 non-null  float64\n",
      " 12  native-country   45222 non-null  object \n",
      " 13  income           45222 non-null  object \n",
      "dtypes: float64(4), int64(1), object(9)\n",
      "memory usage: 4.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#Checking out the datatypes of the features\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of records: 45222\n",
      "Individuals making more than $50,000: 11208\n",
      "Individuals making at most $50,000: 34014\n",
      "Percentage of individuals making more than $50,000: 24.78%\n"
     ]
    }
   ],
   "source": [
    "# Total number of records\n",
    "n_records = len(data)\n",
    "\n",
    "# Number of records where individual's income is more than $50,000\n",
    "n_greater_50k = len(data[data['income'] == '>50K'])\n",
    "\n",
    "# Number of records where individual's income is at most $50,000\n",
    "n_at_most_50k = len(data[data['income'] == '<=50K'])\n",
    "\n",
    "# Percentage of individuals whose income is more than $50,000\n",
    "greater_percent = 100 * n_greater_50k / n_records\n",
    "\n",
    "# Print the results\n",
    "print(\"Total number of records: {}\".format(n_records))\n",
    "print(\"Individuals making more than $50,000: {}\".format(n_greater_50k))\n",
    "print(\"Individuals making at most $50,000: {}\".format(n_at_most_50k))\n",
    "print(\"Percentage of individuals making more than $50,000: {:.2f}%\".format(greater_percent))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize the relationship between different features of an individual, and their incomes.\n",
    "\n",
    "Let's see breakdown of the counts of people earning above or below 50K based on their sex and education levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1511.72x1440 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "sns.catplot(x=\"sex\", col='education_level', data=data, hue='income', kind=\"count\", col_wrap=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Preparing the Data\n",
    "Before data can be used as input for machine learning algorithms, it often must be cleaned, formatted, and restructured — this is typically known as **preprocessing**. Fortunately, for this dataset, there are no invalid or missing entries we must deal with, however, there are some qualities about certain features that must be adjusted. This preprocessing can help tremendously with the outcome and predictive power of nearly all learning algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming Skewed Continuous Features\n",
    "A dataset may sometimes contain at least one feature whose values tend to lie near a single number, but will also have a non-trivial number of vastly larger or smaller values than that single number.  Algorithms can be sensitive to such distributions of values and can underperform if the range is not properly normalized. With the census dataset two features fit this description: '`capital-gain'` and `'capital-loss'`. \n",
    "\n",
    "Let's plot a histogram of these two features and see how they are distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sajal/projects/github/sajal2692/data-science-portfolio/finding_donors/visuals.py:48: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1224x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the data into features and target label\n",
    "income_raw = data['income']\n",
    "features_raw = data.drop('income', axis = 1)\n",
    "\n",
    "# Visualize skewed continuous features of original data\n",
    "vs.distribution(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For highly-skewed feature distributions such as `'capital-gain'` and `'capital-loss'`, it is common practice to apply a <a href=\"https://en.wikipedia.org/wiki/Data_transformation_(statistics)\">logarithmic transformation</a> on the data so that the very large and very small values do not negatively affect the performance of a learning algorithm. Using a logarithmic transformation significantly reduces the range of values caused by outliers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1224x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Log-transform the skewed features\n",
    "skewed = ['capital-gain', 'capital-loss']\n",
    "features_raw[skewed] = data[skewed].apply(lambda x: np.log(x + 1))\n",
    "\n",
    "# Visualize the new log distributions\n",
    "vs.distribution(features_raw, transformed = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalizing Numerical Features\n",
    "In addition to performing transformations on features that are highly skewed, it is often good practice to perform some type of scaling on numerical features. Applying a scaling to the data does not change the shape of each feature's distribution (such as `'capital-gain'` or `'capital-loss'` above); however, normalization ensures that each feature is treated equally when applying supervised learners. Note that once scaling is applied, observing the data in its raw form will no longer have the same original meaning, as exampled below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.30137</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.02174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age   workclass education_level  education-num  marital-status  \\\n",
       "0  0.30137   State-gov       Bachelors            0.8   Never-married   \n",
       "\n",
       "      occupation    relationship    race    sex  capital-gain  capital-loss  \\\n",
       "0   Adm-clerical   Not-in-family   White   Male       0.02174           0.0   \n",
       "\n",
       "   hours-per-week  native-country  \n",
       "0        0.397959   United-States  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import sklearn.preprocessing.StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Initialize a scaler, then apply it to the features\n",
    "scaler = MinMaxScaler()\n",
    "numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
    "features_raw[numerical] = scaler.fit_transform(data[numerical])\n",
    "\n",
    "# Show an example of a record with scaling applied\n",
    "display(features_raw.head(n = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing\n",
    "\n",
    "From the table in **Exploring the Data** above, we can see there are several features for each record that are non-numeric. Typically, learning algorithms expect input to be numeric, which requires that non-numeric features (called *categorical variables*) be converted. One popular way to convert categorical variables is by using the **one-hot encoding** scheme. One-hot encoding creates a _\"dummy\"_ variable for each possible category of each non-numeric feature. \n",
    "\n",
    "Additionally, as with the non-numeric features, we need to convert the non-numeric target label, `'income'` to numerical values for the learning algorithm to work. Since there are only two possible categories for this label (\"<=50K\" and \">50K\"), we can avoid using one-hot encoding and simply encode these two categories as `0` and `1`, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103 total features after one-hot encoding.\n"
     ]
    }
   ],
   "source": [
    "# One-hot encode the 'features_raw' data using pandas.get_dummies()\n",
    "features = pd.get_dummies(features_raw)\n",
    "\n",
    "# Encode the 'income_raw' data to numerical values\n",
    "income = income_raw.apply(lambda x: 1 if x == '>50K' else 0)\n",
    "\n",
    "# Print the number of features after one-hot encoding\n",
    "encoded = list(features.columns)\n",
    "print(\"{} total features after one-hot encoding.\".format(len(encoded)))\n",
    "\n",
    "# Uncomment the following line to see the encoded feature names \n",
    "# print encoded\n",
    "# Left uncommented due to output size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shuffle and Split Data\n",
    "\n",
    "Now all _categorical variables_ have been converted into numerical features, and all numerical features have been normalized. As always, we will now split the data (both features and their labels) into training and test sets. 80% of the data will be used for training and 20% for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 36177 samples.\n",
      "Testing set has 9045 samples.\n"
     ]
    }
   ],
   "source": [
    "# Import train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the 'features' and 'income' data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, income, test_size = 0.2, random_state = 0)\n",
    "\n",
    "# Show the results of the split\n",
    "print(\"Training set has {} samples.\".format(X_train.shape[0]))\n",
    "print(\"Testing set has {} samples.\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Evaluating Model Performance\n",
    "In this section, we will investigate four different algorithms, and determine which is best at modeling the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics and the Naive Predictor\n",
    "\n",
    "*CharityML*, equipped with their research, knows individuals that make more than \\$50,000 are most likely to donate to their charity. Because of this, *CharityML* is particularly interested in predicting who makes more than \\$50,000 accurately. It would seem that using **accuracy** as a metric for evaluating a particular model's performace would be appropriate. Additionally, identifying someone that *does not* make more than \\$50,000 as someone who does would be detrimental to *CharityML*, since they are looking to find individuals willing to donate. Therefore, a model's ability to precisely predict those that make more than \\$50,000 is *more important* than the model's ability to **recall** those individuals. We can use **F-beta score** as a metric that considers both precision and recall:\n",
    "\n",
    "$$ F_{\\beta} = (1 + \\beta^2) \\cdot \\frac{precision \\cdot recall}{\\left( \\beta^2 \\cdot precision \\right) + recall} $$\n",
    "\n",
    "In particular, when $\\beta = 0.5$, more emphasis is placed on precision.\n",
    "\n",
    "Looking at the distribution of classes (those who make at most \\$50,000, and those who make more), it's clear most individuals do not make more than \\$50,000. This can greatly affect **accuracy**, since we could simply say *\"this person does not make more than \\$50,000\"* and generally be right, without ever looking at the data! Making such a statement would be called **naive**, since we have not considered any information to substantiate the claim. It is always important to consider the *naive prediction* for your data, to help establish a benchmark for whether a model is performing well. That been said, using that prediction would be pointless: If we predicted all people made less than \\$50,000, *CharityML* would identify no one as donors. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Predictor Performace\n",
    "\n",
    "What if we chose a model that always predicted an individual made more than \\$50,000, what would that model's accuracy and F-score be on this dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]\n"
     ]
    }
   ],
   "source": [
    "# Calculate accuracy\n",
    "accuracy = n_greater_50k / n_records\n",
    "\n",
    "# Calculating precision\n",
    "precision = n_greater_50k / (n_greater_50k + n_at_most_50k)\n",
    "\n",
    "#Calculating recall\n",
    "recall = n_greater_50k / (n_greater_50k + 0)\n",
    "\n",
    "# Calculate F-score using the formula above for beta = 0.5\n",
    "fscore =  (1  + (0.5*0.5)) * ( precision * recall / (( 0.5*0.5 * (precision))+ recall))\n",
    "\n",
    "# Print the results \n",
    "print(\"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]\".format(accuracy, fscore))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Supervised Learning Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Model Application\n",
    "\n",
    "Now we'll pick three supervised learning models above that are appropriate for this problem, and test them on the census data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Decision Trees**\n",
    "\n",
    " - Real world application: Decision Trees and, in general, CART (Classification and Regression Trees) are often used in financial analysis. A concrete example of it is: for predicting which stocks to buy based on past peformance. [Reference](https://ir.nctu.edu.tw/bitstream/11536/11962/1/000237645100007.pdf)\n",
    " - Strengths: \n",
    "      - Able to handle categorical and numerical data.\n",
    "      - Doesn't require much data pre-processing, and can handle data which hasn't been normalized, or encoded for Machine Learning Suitability.\n",
    "      - Simple to understand and interpret.\n",
    " - Weaknesses:\n",
    "     - Complex Decision Trees do not generalize well to the data and can result in overfitting.\n",
    "     - Unstable, as small variations in the data can result in a different decision tree. Hence they are usually used in an ensemble (like Random Forests) to build robustness.\n",
    "     - Can create biased trees if some classes dominate.\n",
    " - Candidacy: Since a decision tree can handle both numerical and categorical data, it's a good candidate for our case (although, the pre-processing steps might already mitigate whatever advantage we would have had). It's also easy to interpret, so we will know what happens under the hood to interpret the results.\n",
    "\n",
    "\n",
    "**Support Vector Machines (SVM)**\n",
    "\n",
    " - Real world application:  Example of a real world use of SVMs include image classification and image segmentation. For example: Face detection in an image. [Reference](http://www.cmlab.csie.ntu.edu.tw/~cyy/learning/papers/SVM_FaceCVPR1997.pdf)\n",
    " - Strenghs: \n",
    "     - Effective in high dimensional spaces, or when there are a lot of features.\n",
    "     - Kernel functions can be used to adapt to different cases, and can be completely customized if needed. Thus SVMs are versatile.\n",
    " - Weaknesses: \n",
    "     - Doesn't perform well with large datasets. \n",
    "     - Doesn't directly provide probability estimates.\n",
    " - Candidacy: SVMs were chosen because of their effectiveness given high dimensionality. After incorporating dummy variables, we have more than 100 features in our dataset, so SVMs should be a classifier that works regardless of that. Also, our dataset is not that large to be a deterrent. \n",
    "  \n",
    "\n",
    "**Ensemble methods: AdaBoost**\n",
    "\n",
    " - Real world application: Ensemble methods are used extensively in Kaggle competitions, usually in image detection. A real world example of Adaboost is object detection in image, ex: identifying players during a game of basketball. [Reference](https://uni-obuda.hu/journal/Markoski_Ivankovic_Ratgeber_Pecev_Glusac_57.pdf)\n",
    " - Strength: \n",
    "     - Ensemble methods, including Adaboost are more robust than single estimators, have improved generalizability. \n",
    "     - Simple models can be combined to build a complex model, which is computationally fast. \n",
    " - Weaknesses:\n",
    "     - If we have a biased underlying classifier, it will lead to a biased boosted model.\n",
    " - Candidacy: Ensemble methods are considered to be high quality classifiers, and adaboost is the one of most popular boosting algorithms. We also have a class imbalance in our dataset, which boosting might be robust to."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Training and Predicting Pipeline\n",
    "\n",
    "To properly evaluate the performance of each model we've chosen, it's important that you create a training and predicting pipeline that allows you to quickly and effectively train models using various sizes of training data and perform predictions on the testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import two metrics from sklearn - fbeta_score and accuracy_score\n",
    "\n",
    "from sklearn.metrics import fbeta_score, accuracy_score\n",
    "\n",
    "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): \n",
    "    '''\n",
    "    inputs:\n",
    "       - learner: the learning algorithm to be trained and predicted on\n",
    "       - sample_size: the size of samples (number) to be drawn from training set\n",
    "       - X_train: features training set\n",
    "       - y_train: income training set\n",
    "       - X_test: features testing set\n",
    "       - y_test: income testing set\n",
    "    '''\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    # Fit the learner to the training data using slicing with 'sample_size'\n",
    "    start = time() # Get start time\n",
    "    learner = learner.fit(X_train[:sample_size],y_train[:sample_size])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # Calculate the training time\n",
    "    results['train_time'] = end - start\n",
    "        \n",
    "    # Get the predictions on the test set,\n",
    "    #       then get predictions on the first 300 training samples\n",
    "    start = time() # Get start time\n",
    "    predictions_test = learner.predict(X_test)\n",
    "    predictions_train = learner.predict(X_train[:300])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # Calculate the total prediction time\n",
    "    results['pred_time'] = end - start\n",
    "            \n",
    "    # Compute accuracy on the first 300 training samples\n",
    "    results['acc_train'] = accuracy_score(y_train[:300],predictions_train)\n",
    "        \n",
    "    # Compute accuracy on test set\n",
    "    results['acc_test'] = accuracy_score(y_test,predictions_test)\n",
    "    \n",
    "    # Compute F-score on the the first 300 training samples\n",
    "    results['f_train'] = fbeta_score(y_train[:300],predictions_train,beta=0.5)\n",
    "        \n",
    "    # Compute F-score on the test set\n",
    "    results['f_test'] = fbeta_score(y_test,predictions_test,beta=0.5)\n",
    "       \n",
    "    # Success\n",
    "    print(\"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size))\n",
    "        \n",
    "    # Return the results\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation\n",
    "\n",
    "Let's train and test the models on training sets of different sizes to see how it affects their runtime and predictive performance (both on the test, and training sets)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier trained on 362 samples.\n",
      "DecisionTreeClassifier trained on 3618 samples.\n",
      "DecisionTreeClassifier trained on 36177 samples.\n",
      "SVC trained on 362 samples.\n",
      "SVC trained on 3618 samples.\n",
      "SVC trained on 36177 samples.\n",
      "AdaBoostClassifier trained on 362 samples.\n",
      "AdaBoostClassifier trained on 3618 samples.\n",
      "AdaBoostClassifier trained on 36177 samples.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x792 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the three supervised learning models from sklearn\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "# Initialize the three models, the random states are set to 101 so we know how to reproduce the model later\n",
    "clf_A = DecisionTreeClassifier(random_state=101)\n",
    "clf_B = SVC(random_state = 101)\n",
    "clf_C = AdaBoostClassifier(random_state = 101)\n",
    "\n",
    "# Calculate the number of samples for 1%, 10%, and 100% of the training data\n",
    "samples_1 = int(round(len(X_train) / 100))\n",
    "samples_10 = int(round(len(X_train) / 10))\n",
    "samples_100 = len(X_train)\n",
    "\n",
    "# Collect results on the learners\n",
    "results = {}\n",
    "for clf in [clf_A, clf_B, clf_C]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    results[clf_name] = {}\n",
    "    for i, samples in enumerate([samples_1, samples_10, samples_100]):\n",
    "        results[clf_name][i] = \\\n",
    "        train_predict(clf, samples, X_train, y_train, X_test, y_test)\n",
    "\n",
    "# Run metrics visualization for the three supervised learning models chosen\n",
    "vs.evaluate(results, accuracy, fscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also print out the values in the visualizations above to examine the results in more detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1%</th>\n",
       "      <th>10%</th>\n",
       "      <th>100%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_time</th>\n",
       "      <td>0.009007</td>\n",
       "      <td>0.020261</td>\n",
       "      <td>0.187108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pred_time</th>\n",
       "      <td>0.012180</td>\n",
       "      <td>0.004545</td>\n",
       "      <td>0.009989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_train</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996667</td>\n",
       "      <td>0.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_test</th>\n",
       "      <td>0.754008</td>\n",
       "      <td>0.806081</td>\n",
       "      <td>0.817026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_train</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997191</td>\n",
       "      <td>0.963855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_test</th>\n",
       "      <td>0.505562</td>\n",
       "      <td>0.602673</td>\n",
       "      <td>0.624711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  1%       10%      100%\n",
       "train_time  0.009007  0.020261  0.187108\n",
       "pred_time   0.012180  0.004545  0.009989\n",
       "acc_train   1.000000  0.996667  0.970000\n",
       "acc_test    0.754008  0.806081  0.817026\n",
       "f_train     1.000000  0.997191  0.963855\n",
       "f_test      0.505562  0.602673  0.624711"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1%</th>\n",
       "      <th>10%</th>\n",
       "      <th>100%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_time</th>\n",
       "      <td>0.005656</td>\n",
       "      <td>0.286829</td>\n",
       "      <td>38.897411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pred_time</th>\n",
       "      <td>0.225797</td>\n",
       "      <td>1.786065</td>\n",
       "      <td>15.584566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_train</th>\n",
       "      <td>0.846667</td>\n",
       "      <td>0.846667</td>\n",
       "      <td>0.843333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_test</th>\n",
       "      <td>0.812825</td>\n",
       "      <td>0.826645</td>\n",
       "      <td>0.837148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_train</th>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.703125</td>\n",
       "      <td>0.690299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_test</th>\n",
       "      <td>0.610597</td>\n",
       "      <td>0.650518</td>\n",
       "      <td>0.674826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  1%       10%       100%\n",
       "train_time  0.005656  0.286829  38.897411\n",
       "pred_time   0.225797  1.786065  15.584566\n",
       "acc_train   0.846667  0.846667   0.843333\n",
       "acc_test    0.812825  0.826645   0.837148\n",
       "f_train     0.714286  0.703125   0.690299\n",
       "f_test      0.610597  0.650518   0.674826"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostClassifier\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1%</th>\n",
       "      <th>10%</th>\n",
       "      <th>100%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_time</th>\n",
       "      <td>0.028595</td>\n",
       "      <td>0.121050</td>\n",
       "      <td>1.104730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pred_time</th>\n",
       "      <td>0.083909</td>\n",
       "      <td>0.090622</td>\n",
       "      <td>0.058476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_train</th>\n",
       "      <td>0.896667</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_test</th>\n",
       "      <td>0.810392</td>\n",
       "      <td>0.849862</td>\n",
       "      <td>0.857601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_train</th>\n",
       "      <td>0.811688</td>\n",
       "      <td>0.680147</td>\n",
       "      <td>0.711538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_test</th>\n",
       "      <td>0.610473</td>\n",
       "      <td>0.701882</td>\n",
       "      <td>0.724551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  1%       10%      100%\n",
       "train_time  0.028595  0.121050  1.104730\n",
       "pred_time   0.083909  0.090622  0.058476\n",
       "acc_train   0.896667  0.840000  0.850000\n",
       "acc_test    0.810392  0.849862  0.857601\n",
       "f_train     0.811688  0.680147  0.711538\n",
       "f_test      0.610473  0.701882  0.724551"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Printing out the values\n",
    "for i in results.items():\n",
    "    print(i[0])\n",
    "    display(pd.DataFrame(i[1]).rename(columns={0:'1%', 1:'10%', 2:'100%'}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And visualize the confusion matrix for the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing the confusion matrix for each classifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "for i,model in enumerate([clf_A,clf_B,clf_C]):\n",
    "    cm = confusion_matrix(y_test, model.predict(X_test))\n",
    "    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] # normalize the data\n",
    "\n",
    "    # view with a heatmap\n",
    "    plt.figure(i)\n",
    "    sns.heatmap(cm, annot=True, annot_kws={\"size\":30}, \n",
    "            cmap='Blues', square=True, fmt='.3f')\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.title('Confusion matrix for:\\n{}'.format(model.__class__.__name__));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the results above, out of the three models, AdaBoost is the most appropriate for our task. \n",
    "\n",
    "First and foremost, it is the classifier that performs the best on the testing data, in terms of both the *accuracy* and *f-score*.  It also takes resonably low time to train on the full dataset, which is just a fraction of the 120 seconds taken by SVM, the next best classifier to train on the full training set. So it should scale well even if we have more data.\n",
    "\n",
    "By default, Adaboost uses a decision stump i.e. a decision tree of depth 1 as its base classifier, which can handle categorical and numerical data. Weak learners are relatively faster to train, so the dataset size is not a problem for the algorithm.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### But how does Adaboost work?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Adaboost works by combining several simple learners (for ex: decision trees), to create an ensemble of learners that can predict whether an individual earns above 50k or not.\n",
    "\n",
    "2. Each of the learners, in our case decision trees, are created using \"features\" we have about individuals (eg. age, occupation, education, etc) create a set of rules that can predict a person's income. \n",
    "\n",
    "3. During the training process, which lasts for several rounds, the Adaboost algorithm looks at instances where it has predicted badly, and prioritizes the correct prediction of those instances in the next round of raining.\n",
    "\n",
    "4. With each round, the model finds the best learner (or decision tree) to incorporate into the ensemble, repeating the process for the specified number of rounds, or till we can't improve the predictions further.\n",
    "\n",
    "5. All the learners are then combined to make a final ensembled model, where they each vote to predict if a person earns more than 50k or not. Usually we take the majority of the votes to make a final prediction.\n",
    "\n",
    "6. Using this model with the census information of individuals, we can predict the same information for a potential new donor and predict if they earn more than 50K or not, and thus make a decision on the likeliness of them donating to charity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving our Model: Model Tuning\n",
    "\n",
    "Using grid search (`GridSearchCV`) with different parameter/value combinations, we can tune our model for even better results.\n",
    "\n",
    "For Adaboost, we'll tune the n_estimators and learning rate parameters, and also the base classifier paramters (remember our base classifier for the Adaboost ensemble is a Decision tree!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.8398\n",
      "F-score on testing data: 0.6745\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.8680\n",
      "Final F-score on the testing data: 0.7498\n",
      "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=3,\n",
      "                                                         min_samples_split=6),\n",
      "                   learning_rate=0.1, n_estimators=120)\n"
     ]
    }
   ],
   "source": [
    "# Import 'GridSearchCV', 'make_scorer', and any other necessary libraries\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "# Initialize the classifier\n",
    "clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier())\n",
    "\n",
    "# Create the parameters list you wish to tune\n",
    "parameters = {\n",
    "    'n_estimators':[50, 120],\n",
    "    'learning_rate':[0.1, 0.5, 1.],\n",
    "    'base_estimator__min_samples_split' : np.arange(2, 8, 2),\n",
    "    'base_estimator__max_depth' : np.arange(1, 4, 1)\n",
    "}\n",
    "\n",
    "# Make an fbeta_score scoring object\n",
    "scorer = make_scorer(fbeta_score,beta=0.5)\n",
    "\n",
    "# Perform grid search on the classifier using 'scorer' as the scoring method\n",
    "grid_obj = GridSearchCV(estimator=clf, param_grid=parameters,scoring=scorer)\n",
    "\n",
    "# Fit the grid search object to the training data and find the optimal parameters\n",
    "grid_fit = grid_obj.fit(X_train,y_train)\n",
    "\n",
    "# Get the estimator\n",
    "best_clf = grid_fit.best_estimator_\n",
    "\n",
    "# Make predictions using the unoptimized and model\n",
    "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
    "best_predictions = best_clf.predict(X_test)\n",
    "\n",
    "# Report the before-and-afterscores\n",
    "print(\"Unoptimized model\\n------\")\n",
    "print(\"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5)))\n",
    "print(\"\\nOptimized Model\\n------\")\n",
    "print(\"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions)))\n",
    "print(\"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5)))\n",
    "print(best_clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results:\n",
    "\n",
    "|     Metric     | Benchmark Predictor | Unoptimized Model | Optimized Model |\n",
    "| :------------: | :-----------------: | :---------------: | :-------------: | \n",
    "| Accuracy Score |         0.2478      |        0.8398     |      0.8680     |\n",
    "| F-score        |         0.2917      |        0.6745     |      0.7498     |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optimized model has an accuracy of 0.8702 and F-score of 0.7526. \n",
    "\n",
    "These scores are better than the umpotimized model, while being substantially better than the benchmark predictor. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Feature Importance\n",
    "\n",
    "An important task when performing supervised learning on a dataset like the census data we study here is determining which features provide the most predictive power. By focusing on the relationship between only a few crucial features and the target label we simplify our understanding of the phenomenon, which is most always a useful thing to do. In the case of this project, that means we wish to identify a small number of features that most strongly predict whether an individual makes at most or more than \\$50,000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Feature Relevance Observation\n",
    "\n",
    "We know that there are thirteen available features for each individual on record in the census data.  Of these thirteen records, we can guess which five features might be most important for prediction. Let's give that a go."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In my opinion, are most important for prediction are:\n",
    "\n",
    "1. occupation: Different jobs have different payscales. Some jobs pay higher than others.\n",
    "2. education: People who have completed a higher level of education are better equipped to handle more technical/specialized jobs that pay well.\n",
    "3. age: As people get older, they accumulate greater weatlh.\n",
    "4. workclass: The working class they belong to can also be correlated with how much money they make. \n",
    "5. hours-per-week: If you work more hours per week, you're likely to earn more.\n",
    "\n",
    "These are all ranked according the the impact I believe they have on a person's income. Occupation's ranked number one as different jobs have different payscales. People with higher education are more likely to earn better. \n",
    "\n",
    "Now let's see how our algorithm ranks the features acc. to their importance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y5cqoVasWpk6ditDQUDRv3hyOjo5iD09hREZGIiMjA+3bt1eY3qlTJ/j7+yMyMlIMz7Vr1y5UmR06dMgRprL/087Lx2U3adIEu3fvRnx8PJycnNCiRYscw9+FtXnzZujo6ChMq1q1aq7LdujQAfPmzcPp06fRqVMn/P3330hJSRGDSnh4OMzNzVG+fHmx7atWrYq6desiLCyswMDXvHlzqKio4NSpU+jcuTMiIiKQnJyMZs2aKQS+wrRL06ZNoauri+HDh6NTp05o3rw57Ozs8u19zcuZM2fQuHFjhWlaWloYOHBgjvP3Pmyr+/fvIz4+Hi1atMhxjPr6+iIqKgr37t0Tp2XT19fPN8hcuXIFnTp1Uvjc9O/fv9DnEjo7O2Pjxo2IjIyEubk5QkJCoKOjI9ahqMfwhwYMGIDOnTsrTNPS0hL/LkzZPj4+AICEhATxe/PkyZMA3l+NX9hzhfPyYRtdvXoVaWlpubbRvn37EBMTA1NTU2hoaKBXr17o2LEjWrRogZYtWxZ43FLxYeCjHFRUVDB79mx0794dS5YsEW91ke3NmzdiT9CHKleujLt374qvy5Ytm+vVe7l90Xz4Zfaxy5cvY/r06bh16xbKlSuHhg0b5hiaKkh6ejq8vLygpaWFhQsXQiaTidsil8uxZMkSLFmyJMf7nj9/Loawj6+Qyw6r+bGxscGKFSsAvA98Li4u4jx7e3tMmzYNGRkZuHz5skJvXGJiojgEnVudPpaYmJhrL1Rudfx4X6uoqOR5Ly4VFRVs3rwZfn5+OHHiBPbt2wctLS307dsXEyZMKNRVwq9fv861LpUrVwbwfqj742kFqVSpksKPhML4uGwfHx/o6+sjKCgIp06dgoqKCpo3b465c+cW2KP3MSMjo0K/p3LlyrC1tUVwcLDYS2RtbS32eiQmJiIyMjJHOAKAKlWqFFi+trY2fvzxRxw7dgydO3dGSEgInJycchwzhWkXHR0dbN++HatWrcL+/fuxY8cOlC9fHkOHDhV76QvLyspK7MmTyWQoW7YsatSoAXV19RzLfrgvs8/xGjduHMaNG5dj2efPn+PNmzdQV1fPEbqrVKkink/3sdevXxf685abxo0bo3bt2ggODoa5uTmOHDmCNm3aiN8XRT2GP/Tdd9/l+/kuTNn//vsvpk6dKo4eNGjQQPzuLY577+XWRh9ehPZxnSwtLbF582b4+/tj+/bt2LhxI/T09ODt7V3gjxgqHgx8lCsjIyMMHjwY/v7+Oa4GrVChAl68eJHjPS9evICurm6x1iMpKQnDhw+HpaUl/Pz8xB6shQsX5nnhQG5mzpyJGzduICAgAOXLlxenZ38Benh45Dr8o6+vj9u3bwN4/0vZwMBAnFeYk41tbGyQkJCAyMhI/Pvvv7C3txfn2dnZITU1FaGhobh//77CBRs6Ojpo1aoVfvrppxxl5nZrBn19fVy/fj3H9ISEhEL3muWlatWqmDt3LuRyOa5evYo9e/Zg8+bNMDMzE3ul8pP9mXjx4oXC/sv+DBX3Z6awtLS04OXlBS8vL9y7dw8hISFYvXo1VqxYgRkzZih13c7Ozpg+fTpev36NU6dOKfQsZvcSeXl55XhfYXtl2rZti2nTpuHt27c4evSo2NvzocK2yw8//IDly5cjPT0dERER2LJlCxYvXgxra2vxFITCKFeuXJFDevb7gPfnF384OpDN0NAQISEhyMjIEEcFsiUmJuYaKIH3+/njC2ESExPx3//+F5aWloWqm7OzMwIDAzFo0CBcuXIFo0aNUii/KMdwURRUdva5t7q6ujh06BDq1asHFRUV7NixI99bDWX/EP4wEAqCUOCFJtlttGrVKoXPUrbs7yArKyv88ccfePfuHcLCwrB+/XpMnjwZdnZ2ub6Pihcv2qA8jRw5EjVr1sTSpUsVpltZWSE8PFzhyzIhIQFhYWGF/qIsrHv37uH169dwc3MTw55cLkdoaGihf6Xu3LkTe/bswe+//56jZ1JHRwcNGjRATEwMTExMxH/q6upYunQpnj17BgsLC2hoaCicCJ+ZmYlz584VuO7atWtDX18fW7duRfny5WFsbCzOMzAwQN26dbFnzx5UqFBBoW5WVla4d+8ejI2NxTpVrVoVS5YswZ07d3Ksp0mTJrhz547CLTdu375d5FtwfDy8cvPmTTg6OuK///0vVFRUYGlpidmzZ0NNTU28srIg2fvz4/vA/fXXX1BTU8v1P3Fly8rKgrOzs3hFap06deDh4QFzc3Px6u/ivsfhh9q0aQNBELBs2TKkpaWhbdu24rzstjcyMhLbvn79+li5cmWuF+HkpmXLlsjMzMTatWvx5s2bHFfnAoVrl7Nnz8LOzg4JCQnQ0NCAnZ0dpk6dCgBi+ytzPwHv20ZXVxdxcXEKx2hiYiJWrFiB5ORkcYj5w2P09evXOW6L9CELCwucPXtW4Ur5v/76S7wgqTDb5ezsjMePH2PNmjXQ09NTOAWmqMdwURRUdkJCAh4+fIjevXujfv364rb8/fffCuV8vI3ZPaTx8fHitKtXryoM0+bGzMwM6urqePnypUIb3blzB6tWrQIA7NmzBy1btkRGRgbKlCmDli1bYsyYMcjKylK42I+Uhz18lCdNTU3MnDkzx53YBw4ciP379+OXX34Rr2Rbs2YNNDQ0iv2u7XXq1IG2tjZWr14NuVyO1NRU7Ny5Ezdv3oRMJoMgCOKv0txcvnwZc+fOhZOTExo1aoTIyEgxKGpoaKBRo0bw8vLCyJEjoaOjgzZt2uDVq1dYvnw5VFRUUL9+fZQpUwaDBw/GunXroKWlhYYNG+LPP//EixcvxBO782NtbY2QkBC0aNEiR6Cyt7fHrl270LJlS4Uv3xEjRqBv374YPXo0evTogfT0dKxevRpPnz4VL4L5UOfOnbF27VoMHz4cXl5eyMrKwvLlyyGTyfLdPx/L/qUeGhqKWrVqoV69etDW1saECRPg6emJChUq4MCBA5DJZIW+qq9SpUpwdXXFhg0boKqqiqZNm+LixYvibWMqVKhQ6PoVF1VVVZiammLVqlXQ1NREnTp1EBkZiYiICLF3L7un6NixY3BwcMj1fMhPpaOjgxYtWmD37t1o3ry5wj4YNGgQgoKCMGTIEAwYMADq6urYuHEjrl69WqgrPIH37Whvb4+NGzeibdu2uZ4CUZh2MTU1hSAI8PT0hLu7O9TV1bFlyxaUL19eDDfly5fHjRs3EB4eDjMzs3xPz/gUampqGDVqFObPnw/gfc94bGwslixZglq1asHQ0BAymQydO3fG3LlzkZaWhmrVquGPP/7IN6gMHz4cP//8M7y8vNC7d288e/YMy5cvR//+/aGjo5Oj/XNTq1YtGBsbY/fu3ejXr5/C8V3UY7goCiq7cuXKqFatGrZs2YLKlStDVVUVBw4cwOnTpwFA7LErX7483r17h+PHj8PU1BRGRkYwMDDAihUroKamhuTkZPj6+uZ6U/EPZX+W5s+fj9evX8PU1BQ3b97EsmXL0KpVK+jo6KBJkyZ48eIFRo8ejX79+iEjIwNr1qyBoaFhrqcIUfFjDx/ly9bWNseVnlWrVsWOHTugr6+PiRMnYsqUKahevTp27dpV7De7LVeuHPz8/PDmzRt4eHhg5syZ0NXVxYoVKyCXyxEZGZnv+8+dO4eMjAycOnUK3bp1Q+/evdGnTx/06dNHvCqyVatWWL16NaKjo+Hh4YG5c+fC3NwcW7duFS+wGD16NDw9PbFjxw54eXmhXLlyOc5tzIutrS0yMjIUhmyz2dnZISMjA7a2tgrTjY2NsWXLFrx69QpeXl6YMmUKDAwMsG3btlyHPtTV1bFhwwZ89913GD9+PObOnYv+/fujatWqRTo5W0dHB+7u7jh48CC8vb2hpqaGdevW4fvvv8f06dMxbNgw3Lt3D3/88UeRbvzs7e2NMWPG4NChQxg2bBiOHDmCCRMmKNx+50vz8fFBly5dsHbtWgwePBj79u3DhAkTxFtw2NnZwdHREbNmzcLGjRuLff0uLi5iT+OHqlWrhp07d6JMmTLw9vbG2LFjIZfLsWnTpiL9x9i2bdtcL8r4UEHtoquri/Xr10NTUxPjx4+Hp6cn0tLSsGnTJvEcroEDByI9PR1DhgzJ9bSC4tC/f39Mnz4dJ0+ehLu7O1asWCHeWij7B82cOXPQq1cv+Pn54bfffoOZmVm+V9Kbm5tjw4YNeP78OUaOHIk1a9bA1dVVPE+wsO2fVzsW9RguioLKlslk8PPzg7a2NsaOHYvJkyfj3bt32LRpEwCIPZ+dOnVC48aNMWbMGAQFBUFVVRXLly+HhoYGPD09sXLlSnh7exfqh623tzdGjBiBPXv2YMiQIdi6dSvc3NzEoF67dm2sXbsWCQkJ8PLywrhx41C5cmVs2rQpz2F3Kl4ygU9OJvrm3bp1S+HGycD7k+7t7Ozg7e2NAQMGlGDtiIiopHFIl0gCkpKSMGLECAwfPhz29vZITk7G5s2boa2tjU6dOpV09YiIqISxh49IIg4ePIiNGzfiwYMHUFdXR5MmTfDbb7+hbt26JV01IiIqYQx8RERERBLHizaIiIiIJO6bPIdPLpcjJSUF6urqRbrlBBEREZEUCYKAjIwMaGtr53ofyW8y8KWkpIhPPyAiIiKi9+rXr5/rvRO/ycCXfc+e+vXri88tlLLo6GiFJzTQt43tKT1sU2lhe0pPaWjT9PR03L59O8/7Gn6TgS97GFdDQyPXO8hLUWnZztKC7Sk9bFNpYXtKT2lp07xOdeNFG0REREQSx8BHREREJHHf5JAuERVdRkYGYmNjkZqaWtJVkRw1NTXcuHGjpKshKVpaWjA0NORzVomKCQMfUSkRGxuLcuXKoVatWrydUTFLSUmBtrZ2SVdDMgRBwMuXLxEbG4vatWuXdHWIJIFDukSlRGpqKipXrsywR189mUyGypUrszeaqBgx8BGVIgx79K3gZ5WoeDHwEREREUkcAx9RKZaapZwhs4LKjY2NhbGxMbp06aLw7+nTp0VeV0xMDCZPnvypVc3ToEGDcOzYMfH1ggULYGFhgfT0dHGao6MjYmJi8iyjS5cu+a4jMDAQEydOzDE9KSkJI0aM+IRaExHljhdtEJViWqpakG0p/qEzwU0ocBl9fX0EBQV99rqePHmSb+j6VHZ2drh8+TLatGkDAAgNDYWZmRkiIiJgZ2eHhw8fomzZsqhRowZSUlJyLeNTt+/169e4efPmJ9ediOhj7OEjoq/KixcvMGLECHTv3h09evRAaGgoACAuLg6DBw9G79694eTkhMWLFwMAZs+ejejoaMyYMQPh4eFwdXUVy5o4cSICAwMRGxuL9u3b46effsLAgQORlZWFefPmoVu3bujcuTM2b96cox62tra4cuWKuG4NDQ20b98e//zzDwDg0qVLsLe3BwAcPnwY3bp1Q5cuXTB58mSkpaUBAIyMjAC877Hz8PBAp06dMHz4cHTt2hWxsbEAgIcPH8LV1RWtWrWCj4+PuE3x8fEYOXJkce9eIiqlGPiIqETEx8crDOeuX78eADBnzhz06NEDgYGBWLNmDX7//XckJyfj8OHDcHZ2xu7du3Hw4EHs3LkTCQkJ8PHxgbGxMaZNm5bv+u7fv49FixZh8+bN2L17NwBg//792Lt3L06cOIFLly4pLN+4cWM8evQIaWlp+Oeff+Dg4AAHBweFwOfo6Ig7d+4gMDAQu3btQlBQECpXrowNGzYolLVq1SrUrl0b//nPfzBy5EjcunVLnPf06VP4+fnhyJEjOHv2LO7cuQMfHx/o6+tj1apVn72fiYpLalbWN11+acchXSIqEXkN6YaGhuLevXvw9fUFAGRmZiImJgaDBw/G+fPnsWHDBty5cwcZGRl49+5doddXuXJlGBoaAgDCwsJw48YNnD9/HgDw9u1b3Lp1C02aNBGXV1VVhZmZGa5du4Z//vkHP//8M2rUqIHU1FS8fv0aV65cweTJkxEUFISYmBj07t0bwPsbXDdq1Ehh3efOnRN7JE1MTMSePwBo0qQJdHV1AQA1a9bEq1evUKZMmUJvF9GXoqWqCtmWLUorX3BzU1rZxMBHRF8ZuVyOLVu2iCEoLi4Oenp6mD9/PmJiYuDs7IzWrVsjNDQUgqB4rqBMJlOYlpGRIf6tpaUl/p2VlQVvb2+0bdsWAJCQkICyZcvmqEv2eXxRUVFYtGiROO3EiRPQ1dVFuXLlkJWVhTZt2mDGjBkA3t+EOeujngpVVdUcdc2mpva/r+GP609EVFw4pEtEXxVbW1vs3LkTAHD37l107twZ7969w7lz5zB48GB06NABT58+RVxcHORyOVRVVZGZmQkAqFixImJiYpCWlobExERERETkuY7du3cjIyMDKSkp6NevHyIjI3NdLigoCPXr1xeDmYODAzZt2gQHBwcAgI2NDU6dOoWXL19CEARMnz4dWz7qBbG3t8ehQ4cAALdu3cKdO3fyvc+cmpqauE1ERMWBPXxE9FXx8fHB77//DhcXFwDAwoULoaOjg2HDhmH8+PEoX748KleuDGNjY8TGxqJhw4ZISkqCt7c3Fi1ahObNm6NTp06oXr06rKyscl1H37598fDhQ3Tr1g2ZmZno3r07bGxscixXv359JCYmol+/fuI0W1tbjBkzRgx8DRo0wNChQ+Hm5ga5XI6GDRti6NChCuWMGDECkyZNgouLC2rWrAk9PT2FHsePVa5cGdWqVYOrqyu2bdtW5H1IRPQxmfANjh+kpaUhOjoaxsbG0NTULOnqKF1ERESe/3HRt6ek2vPGjRto2LChwrTUrFRoqeYdPD6Vssr9WhX0LN2goCAYGhrCysoKT548Qf/+/XH8+HGoqHCQJT+5fWa/BH7n5u1bPYevNLRpQdmIPXxEpZiyQllpCnuFUadOHUybNg1yuRwqKiqYOXMmwx4RfVEMfERESmZiYoLAwMCSrgYRlWL8iUlEREQkcQx8RERERBLHwEdEREQkcQx8RERERBLHwEdEREQkcQx8RKWYsh5WXphyk5OTMWPGDDg7O6NLly5wdXXFf//7X6XUJ5urqyvCw8MLXCZbly5dimWdbdq0QZcuXcR/O3bs+KSyJk2ahMePH392nT508OBBeHh4iK9v374NIyMjHDx4UJy2ZMkS8dnGuVmxYgVOnDiR73o+fH7wh3x9fXHp0qUi1pqIioq3ZSEqxZT1MPSCbqAql8vh7u4OGxsbHDhwAGpqajh//jzc3d3xn//8BxUrViz2OhXWhQsXxL+DgoKKpczZs2fn+iSPogoPD8fIkSOLoUb/Y2tri3nz5omv//nnHzg6OuKff/5B586dAQCXLl3Cb7/9lmcZo0eP/uT1X7x4sVj2DRHljz18RPTFhYeHIz4+Hl5eXuIzarODh1wuR3h4uEJP28SJExEYGIjY2Fh06dIFnp6eaNu2LX799Vfs2rULffr0Qfv27fHvv/8CAFq2bInY2FhxXR+WBQCZmZnw8fFBnz590KpVKwwZMgSpqamYPXs2AKBXr14A3vdKZWZmwtHRES9evAAAJCYmwtHRERkZGTh79ix69uyJn376CZ6ennj16lWR9oO/vz+6deuGzp07Y+HChch+8NGyZcvQu3dvtGvXDn379sXz58/h7++P+Ph4DB06FK9evcpzG11dXeHp6Yl27drhxo0bYh27du2aax319fVRsWJF3L9/H8D7wDd69GhcuHABgiAgLS0NDx48gJmZGR4+fIhBgwahW7du+Omnn3D9+nWF9gGArVu3om3btujRowe8vb3h5+cnruv3339H586d0blzZzx8+BAHDhxAdHQ0fHx8cOvWrSLtOyIqGgY+Ivrirl+/DhMTkxxPm2jevDkqV66c73tv3bqFESNGIDg4GNeuXcPjx48REBAAZ2dnBAQEFGr9V65cgbq6OgICAnDs2DGkpaXhzJkz8PHxAQDs2bNHXFZNTQ3t27dHcHAwAODo0aNo3bo1kpKSsGTJEmzYsAF//vknHB0dsXjx4lzX5+PjIw7nZj+X9+zZs4iOjsbevXtx4MABxMXF4eDBg3j48CHu3buHXbt2ISQkBDVr1sShQ4cwdOhQ6Ovrw9/fv8AeUCMjI4SEhMDAwECs44EDB/Kso52dHS5fvozU1FTExsbC1NQUhoaGuHnzJiIjI2FhYQE1NTVMmDAB3t7e2L9/P2bNmoWxY8cqlHPz5k3s2LEDgYGB2LlzJx4+fKgw397eHgcPHoSDgwN27dqFrl27wtjYGLNnz85zyJeIiodSh3QPHTqENWvWICMjAwMHDsTPP/+sMH/lypXYt28fypcvDwDo3bt3jmWISHpUVFTwqY/x1tPTQ6NGjQAA3333Hezs7AAA1apVE3u8CtK0aVPo6upix44duHfvHh48eIC3b9/muXyXLl0wd+5c9O/fH4cPH8aYMWMQGRmJp0+fYsCAAZDL5QCAChUq5Pr+3IZ0w8LCEBUVhe7duwMAUlNTUa1aNXTp0gUTJkzAnj17cP/+fVy9ehU1a9Ys1HZlMzU1BQCFOgLvh9Jzq6OtrS1Onz6NKlWqoEmTJgDeh7Pw8HC8ffsWDg4OSElJQXR0NCZNmiS+7+3btwo9hmFhYXBycoKOjg4AoFOnTnjz5o04v3Xr1gCAevXq8bw9oi9MaYEvLi4Oy5YtQ2BgIDQ0NNC3b1/Y2NigXr164jLR0dFYunQpLCwslFUNIvoKGRsbY+fOnRAEATKZTJy+dOlS2Nvb5wiEGRkZ4t8aGhoKZamqqua6juz3Z2Zm5ph34sQJ+Pr6YsCAAejevTtevXqVbwA1MTHB69evERUVhbi4OFhaWuL48eOwtLTE2rVrkZKSAjU1NaSkpBRuBwDIysqCm5sbBg0aBAB48+YNVFVVER0djXHjxmHgwIFo165dvuE4r23U0tIS15FdR+D9w9Vzq6O1tTV8fX2ho6MDR0dHAICjoyM2b96M169fY+rUqZDL5dDQ0FA4r/HZs2fQ1dUVX6uoqIjhNzfZw/cymeyTAz8RfRqlDemGhobC1tYWurq6KFu2LNq1aycOiWSLjo7GunXr4OLigpkzZyItLU1Z1SGir0iTJk1QuXJlrFy5Eln/f0Xv33//jcDAQNSrVw8VK1ZETEwM0tLSkJiYiIiIiCKVX7FiRdy9excAcr16NCwsDB06dECPHj2gp6eHixcvivVQVVXNNSS6uLhg2rRp6NixIwDAzMwMV69eFc99W716NRYuXFjoOtra2iIoKAgpKSnIzMzEyJEjERISgosXL8La2ho//fQT6tWrh3PnzinULfvvgraxKHWsUKECtLS08Pfff4s9psbGxrh37x7i4+NRq1YtlCtXDrVq1RID37lz53KMyNjZ2eHMmTNITk5Geno6jh49qhDoc/PhNhGR8iithy8+Ph5VqlQRX+vr6yMqKkp8nZKSgoYNG2LChAmoXr06Jk6ciNWrV+c4JyQ/0dHRxVrnr1lR/8Ojr1tJtGduPVAyDY0Cr6j9FG8zMiCkp+e7zOLFi7FkyRJ07NgRampq0NXVxYoVK1CmTBmUKVMGDg4O6NixI6pWrQpzc3OkpaXh3bt3kMvl4nZkZWUhNTUVKSkpSEtLQ2ZmJlJSUjB06FDMnj0bvr6+sLOzQ1ZWFlJSUsTlnZ2dMWXKFPz1119QV1eHsbEx7t+/j5SUFDRv3hwuLi7irVOy19W6dWusWLECc+bMQUpKCsqWLYvff/8dXl5ekMvl0NfXx+zZs3Ps4w/r+CEbGxs4OTmhZ8+ekMvlsLOzQ9u2bfH8+XMcPnwYnTp1gpqaGurWrYsHDx4gJSUFDg4OGDJkCFatWlXgNhaljgBgYWGBixcvQkNDQ5xfvXp1lCtXTnw9c+ZMzJ07F/7+/lBXV8e8efPw9u1bZGZmIi0tDdWrV0efPn3Qq1cvlC1bFpUqVYKmpma+n4Mff/wR06ZNw4IFC2BpaZnvsh9KSk3FbSXexoffuTlZWVkpfR3K3O+lvU1lgpL61deuXYt3796JAW7Pnj24du0aZs6cmevy169fx+TJk3HgwIECy05LS0N0dDSMjY0L/DKRgoiIiC9yoNGXUVLteePGDTRs2PCLr7c0SElJgba2dklXo8Tdv38fZ86cwcCBAwEAHh4esGjXDpb/P0xcVC/u30eH/78S+GPK+KGSjd+5eVPGbZyysU0/T0HZSGlDugYGBuJtDID3PX76+vri6ydPnmDv3r3ia0EQxPM7iIjo21O9enVcu3YNzs7OcHFxQa1atWDh4FDS1SIiKHFI197eHn5+fkhISECZMmVw9OhRzJo1S5yvpaWFRYsWwcbGBoaGhtixYwfatGmjrOoQEZGSaWhoYMmSJQrTLn3ww5+ISo5Se/jGjh2LAQMGoGvXrnB2doapqSnc3d1x7do1VKpUCTNnzoSHhwfat28PQRDEq9WISDl4ZSR9MwQBeV/vS0RFpdQxVBcXF7i4uChMW7dunfh3u3bt0K5dO2VWgYj+n6qqKjIyMnLc1oToq5SVhWRevUtUbPikDaJSQldXF3FxcfneJ43oqyAISElIwIGXL0u6JkSSwaskiEoJPT09xMbG8pmlSpCens6e0zy8SE4u8nuyAFxJTsYeBj6iYsPAR1RKqKioFPkRXVQ4ERERMDMzK+lqfJUaKfE2HkRUeBzSJSIiIpI4Bj4iIiIiiWPgIyIiIpI4Bj4iIiIiiWPgIyIiIpI4Bj4iIiIiiWPgIyIiIpI4Bj4iIiIiiWPgIyIiIpI4Bj4iIiIiiWPgIyIiIpI4Bj4iIiIiiWPgIyIiIpI4Bj4iIiIiiWPgIyIiIpI4Bj4iIiIiiWPgIyIiIpI4Bj4iIiIiiWPgIyIiIpI4Bj4iIiIiiWPgIyIiIpI4Bj4i+iqkZmV9k2UTEX0L1Eq6AkREAKClqgrZli1KKVtwc1NKuURE3wr28BERERFJHAMfERERkcQx8BERERFJHAMfERERkcQx8BERERFJHAMfERERkcQx8BERERFJHAMfERERkcQx8BERERFJHAMfERERkcQx8BERERFJHAMfERERkcQx8BERERFJHAMfERERkcQpNfAdOnQIHTt2RJs2bbBjx448lzt9+jRatmypzKoQERERlVpqyio4Li4Oy5YtQ2BgIDQ0NNC3b1/Y2NigXr16Csu9ePECCxYsUFY1iIiIiEo9pfXwhYaGwtbWFrq6uihbtizatWuH4ODgHMv5+PjA09NTWdUgIiIiKvWUFvji4+NRpUoV8bW+vj7i4uIUltm6dSsaNWoEMzMzZVWDiIiIqNRT2pCuIAg5pslkMvHv27dv4+jRo9i8eTOePXv2SeuIjo7+5Pp9ayIiIkq6ClSM2J45WVlZKbV8Ze9ztmlO33Kbsj1zUnZ7AmxTZVJa4DMwMMClS5fE1/Hx8dDX1xdfBwcH4/nz5+jRowcyMjIQHx+Pfv36YefOnYVeh7GxMTQ1NYu13l+jiIiIL3Kg0ZfB9iwZytznbNOSoax9zvYsOWzTT5eWlpZvR5jShnTt7e0RFhaGhIQEvHv3DkePHkWzZs3E+V5eXggJCUFQUBD8/f2hr69fpLBHRERERIWjtMBnYGCAsWPHYsCAAejatSucnZ1hamoKd3d3XLt2TVmrJSIiIqKPKG1IFwBcXFzg4uKiMG3dunU5ljM0NMTJkyeVWRUiIiKiUotP2iAiIiKSOAY+IiIiIolj4CMiIiKSOAY+IiIiIolj4CMiIiKSOAY+IiIiIolj4CMiIiKSOAY+IiIiIolj4CMiIiKSOAY+IiIiIolj4CMiIiKSOAY+IiIiIolj4CMiIiKSOAY+IiIiIolj4CMiIiKSOAY++malZmV9k2UTERF9aWolXQGiT6WlqgrZli1KKVtwc1NKuURERCWBPXxEREREEsfAR0RERCRxDHxEREREEsfAR0RERCRxDHxEREREEsfAR0RERCRxDHxEREREEsfAR0RERCRxDHxEREREEsfAR0RERCRxDHxEREREEsfAR0RERCRxDHxEREREEsfAR0RERCRxDHxEREREEsfAR0RERCRxDHxEREREEsfAR0RERCRxDHxEREREEsfAR0RERCRxBQa+IUOG5JjWu3dvpVSGiIiIiIqfWl4zvLy8cP/+fcTExMDFxUWcnpmZCRUVdgwSERERfSvyDHzjx4/H48ePMXXqVEydOlWcrqqqih9++OGLVI6IiIiIPl+egc/Q0BCGhoYIDg5mjx4RERHRNyzPwJftxIkTWLhwIRISEiAIAgRBgEwmw+XLlwss/NChQ1izZg0yMjIwcOBA/Pzzzwrzjx07Bl9fX8jlcpiYmGDmzJnQ0ND49K0hIiIiohwKDHwLFy7EhAkT0KBBA8hkskIXHBcXh2XLliEwMBAaGhro27cvbGxsUK9ePQDA27dvMXPmTOzfvx96enoYO3Ys9u/fjz59+nz61hARERFRDgUGvnLlyqF169ZFLjg0NBS2trbQ1dUFALRr1w7BwcHw9PQEAJQtWxYnT56Euro63r59i5cvX6J8+fJFXg8RERER5a/Ak/NMTExw/PjxIhccHx+PKlWqiK/19fURFxensIy6ujrOnDkDJycnvHr1Co6OjkVeDxERERHlL88ePgsLC8hkMmRlZSEgIAAaGhpQU1Mr9Dl8giDkmJbbkHDz5s0RHh6OpUuXYvr06ViyZEmhKx8dHV3oZb91ERERJV2Fr46VlZVSy1fmPmd75vQtt+eXKP9b9C23KdszJ2W3J8A2VaY8A9/hw4c/q2ADAwNcunRJfB0fHw99fX3xdWJiIqKjo8VePRcXF4wdO7ZI6zA2NoampuZn1fNbEBER8UUONFKkrH3O9iwZytznbNOSwWNUetimny4tLS3fjrA8A1/16tUBAAcOHMgxr0yZMkhNTUXdunXzLNje3h5+fn5ISEhAmTJlcPToUcyaNUucLwgCvL29sW/fPlSrVg1HjhyBpaVlYbaJiIiIiIqgwIs2goKCcPXqVdja2kJVVRVhYWGoUaMG3rx5g2HDhuV5Va2BgQHGjh2LAQMGICMjAz179oSpqSnc3d3h5eUFExMTzJo1C8OGDYNMJkO9evUwY8aMYt9AIiIiotKuwMAnk8mwd+9esTcvJiYGs2fPxvbt29GvX798b6Pi4uKi8Fg2AFi3bp34d+vWrT/pCmAiIiIiKrwCr9J9/vy5wtBtjRo1EBcXBx0dHaiqqiq1ckRERET0+QoMfBUqVEBAQACysrKQmZmJgIAA6Orq4v79+5DL5V+ijkRERET0GQoMfHPmzMH+/fthYmICMzMzHD58GHPmzEFISAg8PDy+RB2JiIiI6DMUeA7f999/j127duHNmzdQUVGBjo4OAGD48OFKrxwRERERfb48A9+cOXMwZcqUPIPd2rVrlVYpIiIiIio+eQY+Ozs7AO+fgUtERERE3648A1/Lli0BAN26dcOzZ89w69YtODo6Ij4+HlWrVv1iFSQiIiKiz1PgRRtnzpxB3759MWPGDLx8+RIdO3bE8ePHv0TdiIiIiKgYFBj4/Pz8sHv3bpQvXx76+vrYuXMnfH19v0TdiIiIiKgYFBj45HI59PX1xdcNGzaETCZTaqWIiIiIqPgUGPjKlCmDJ0+eiCHv0qVL0NTUVHrFiIiIiKh45HnRRnJyMnR0dPDbb7/hl19+wfPnz9GnTx88ePAAfn5+X7KORERERPQZ8gx8tra2sLKyQosWLbBgwQIkJiZCLpfDzMwMlSpV+pJ1JCIiIqLPkGfgO3v2LM6fP4+wsDDs2LEDMpkMzZs3h7q6OqytraGhofEl60lEREREnyjPwFepUiV07NgRHTt2BAA8fvwYoaGhWLx4MR4+fIgrV658sUoSERER0acr8Fm6sbGxOHHiBM6dO4fr16+jcePG6N2795eoGxEREREVgzwD37Jly3Dy5EmkpKTgxx9/RL9+/WBrawstLa0vWT8iIiIi+kx5Br4//vgDLVu2xNChQ2Fubv4Fq0RERERExSnPwBccHIxTp05hyZIlePDgARwcHNCiRQs4OjpCR0fnS9aRiIiIiD5DnjderlWrFgYNGoRt27bhP//5DxwdHXHs2DF06tQJgwYN+pJ1JCIiIqLPUOCTNgDgyZMnSEhIQHp6OtTV1aGqqqrsehERERFRMclzSHfr1q24cOECLl68CF1dXfz444/o2bMnbG1t+Wg1IiIiom9InoHv77//RrNmzeDt7Y3vv//+S9aJiIiIiIpRnoFv3bp1X7IeRERERKQkhTqHj4iIiIi+XQx8RERERBLHwEdEREQkcQx8RERERBLHwEdEREQkcQx8RERERBLHwEdEREQkcQx8RERERBLHwEdEREQkcQx8RERERBLHwEdEREQkcQx8RERERBLHwEdEREQkcQx8RERERBLHwEdEREQkcQx8RERERBLHwEdEREQkcUoNfIcOHULHjh3Rpk0b7NixI8f848ePo0uXLujcuTNGjBiB169fK7M6RERERKWS0gJfXFwcli1bhp07dyIoKAgBAQG4e/euOD85ORnTp0+Hv78/Dh48CCMjI/j5+SmrOkRERESlltICX2hoKGxtbaGrq4uyZcuiXbt2CA4OFudnZGRg+vTpMDAwAAAYGRnh6dOnyqoOERERUamltMAXHx+PKlWqiK/19fURFxcnvq5YsSJat24NAEhNTYW/v7/4moiIiIiKj5qyChYEIcc0mUyWY1pSUhJGjBiBBg0aoFu3bkVaR3R0dKGXrd+4McppaRWp/MJKzcqClqqqUsoGACsrK6WVXVRJqUm4/d/bJV0NAMrfLxEREd9k2d+qb7k9v0T536JvuU3Znjl9if+L2KbKo7TAZ2BggEuXLomv4+Pjoa+vr7BMfHw8Bg8eDFtbW0yePLnI6zA2Noampmahl5dt2VLkdRSG4OYG2ZacYVaKBDfhqwqgyqSs7YyIiCg1+/Brosx9zjYtGTxGpYdt+unS0tLy7QhT2pCuvb09wsLCkJCQgHfv3uHo0aNo1qyZOD8rKwvDhw9Hhw4dMGXKlFx7/4iIiIjo8ym1h2/s2LEYMGAAMjIy0LNnT5iamsLd3R1eXl549uwZrl+/jqysLISEhAB432M3Z84cZVWJiIiIqFRSWuADABcXF7i4uChMW7duHQDAxMQEN2/eVObqiYiIiAh80gYRERGR5DHwEREREUkcAx8RERGRxDHwEREREUkcAx8RERGRxDHwEREREUkcAx8RERGRxDHwEREREUkcAx8RERGRxDHwEREREUkcAx8RERGRxDHwEREREUkcAx8RERGRxDHwEREREUkcAx8RERGRxDHwEX1hVlZWJV0FUWpWaklXgYiIvgC1kq4A0ddKtkVW0lVQOsFNKOkqEBHRF8AePiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjilBr5Dhw6hY8eOaNOmDXbs2JHnchMmTEBgYKAyq0JERERUaikt8MXFxWHZsmXYuXMngoKCEBAQgLt37+ZYZvjw4QgODlZWNYiIiIhKPaUFvtDQUNja2kJXVxdly5ZFu3btcgS7Q4cOoVWrVujQoYOyqkFERERU6qkpq+D4+HhUqVJFfK2vr4+oqCiFZYYMGQIAiIiI+KR1REdHF3pZKyurT1oH5fSp7VXc2KbFo7S0p7K382vZj1+Tb7lN2Z45fYnvXLap8igt8AmCkGOaTCYr1nUYGxtDU1OzWMukgjFoSUtpaU9lbmdERESp2Y9fE2Xtc7ZnyWGbfrq0tLR8O8KUNqRrYGCAFy9eiK/j4+Ohr6+vrNURERERUR6UFvjs7e0RFhaGhIQEvHv3DkePHkWzZs2UtToiIiIiyoNSe/jGjh2LAQMGoGvXrnB2doapqSnc3d1x7do1Za2WiIiIiD6itHP4AMDFxQUuLi4K09atW5djufnz5yuzGkRERESlGp+0QURERCRxDHxEREREEsfAR0RERCRxDHxEREREEsfAR0T0mb6mG7qmZqWWdBWI6Cuk1Kt0iYi+FrItxfukn6+V4JbzKUdEROzhIyIiIpI4Bj4iIiIiiWPgIyIiIpI4Bj4iIiIiiWPgIyIiIpI4Bj4iIiIiiWPgIyIiIpI4Bj4iIiIiiWPgIyIi+gCfnEJSxCdtEBHRN6k0PD2FT06h4sIePiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIpI0Pj2FT9ogIiKirwSfnqI87OEjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjgGPiIiIiKJY+AjIiIikjilBr5Dhw6hY8eOaNOmDXbs2JFj/o0bN9CjRw+0a9cOU6ZMQWZmpjKrQ0RERFQqKS3wxcXFYdmyZdi5cyeCgoIQEBCAu3fvKizj7e2NqVOnIiQkBIIgYPfu3cqqDhEREVGppaasgkNDQ2FrawtdXV0AQLt27RAcHAxPT08AwOPHj5Gamgpzc3MAQPfu3eHr64t+/foVWLYgCACA9PT0ItWpqrp6kZYvrLS0NFRVr6qUsr82aWlpJV0FBWzTz8P2lB62qbSUlvYE2KafKzsTZWekj8mEvOZ8pj/++ANv377F2LFjAQB79uxBVFQUZs2aBQC4cuUKFi5ciD///BMA8PDhQwwdOhQhISEFlp2UlITbt28ro9pERERE36z69eujXLlyOaYrrYcvtxwpk8kKPT8/2traqF+/PtTV1Qv9HiIiIiKpEgQBGRkZ0NbWznW+0gKfgYEBLl26JL6Oj4+Hvr6+wvwXL16Ir58/f64wPz8qKiq5plciIiKi0kpLSyvPeUq7aMPe3h5hYWFISEjAu3fvcPToUTRr1kycX716dWhqaiIiIgIAcODAAYX5RERERFQ8lHYOH/D+tix//PEHMjIy0LNnT7i7u8Pd3R1eXl4wMTHBzZs34ePjg5SUFDRq1Ajz5s2DhoaGsqpDREREVCopNfARERERUcnjkzaIiIiIJI6Bj4iIiEjiGPiIiIiIJI6Bj4iIiEjiGPhKyJ9//ik+ZWTSpEl4/Phxvsu7uroiPDw8x3Q/Pz/4+fkppY5EpVlxHaNFde3aNUyZMuWzy/lWhIeHw9XVtaSr8VUyMjIq6SooVXH//5WUlIQRI0YAAOLi4uDu7l5sZUuB0m68TPn76aefxL/Dw8MxcuTIEqwNEX2spI5RExMTmJiYfJF1EUnJ69evcfPmTQDvH+6wbt26Eq7R14WB7xMIgoDFixfj+PHjUFVVRZ8+fdCwYUMsW7YMqampeP36Nby9vdGhQwdMnDgRMpkMt2/fRnJyMjw8PNC1a1fxV42mpibi4+MxdOhQ7NixA+fPn8emTZuQmpqKtLQ0zJ49G02bNi1UvU6dOoXly5dDLpejRo0amDlzJvT09LBgwQKcO3cOqqqqaNWqFTw9PREWFoZFixYBACpUqIAlS5agUqVKSttnpUVmZiamT5+OO3fu4MWLF6hduzZWrlyJ3bt3Y/v27ShXrhzq1KmDmjVrYtSoUTh79ix8fX2RmZkJQ0NDzJo1CxUrVizpzfjmfU3HaFJSEsaPH49Hjx6hRo0aePbsGVauXAldXV1MnjwZcXFxiI+PR5MmTbBw4UJcuHABK1euxLZt2+Dq6goTExNEREQgISEBPj4+aN68+ZfajV9MQkIC3N3d8ejRI9SuXRu+vr44dOgQNm3aBJlMhsaNG2Pq1KnQ1taGkZERbt26BQAIDAzEhQsXMH/+fLRs2RKmpqa4ceMGNm3ahOnTp4tPcxo5ciRatWqVY722trZwcnJCdHQ0tLW1sXjxYhgaGiIqKgrz5s1DamoqKlasiBkzZqBGjRpwdXVFhQoVcOfOHSxfvhwNGzYUy3JxccHy5ctRt25djBs3Djo6OpgxYwauXr2KVatWYd26dfD398eRI0eQlZUFR0dHeHt7QyaT4cCBA9iyZQvkcjkaN26MadOmQVNTUyz78uXLmDRpEvz9/fH9998ruTWKT27bu2HDBuzevRsVK1ZE+fLlYWpqCgB5tmtoaCjmz58PQRBQrVo1LFmyBAByPXZmz56N+Ph4jBw5EpMmTcKAAQNw8uRJvHjxAlOmTMGTJ0+gpqaGsWPHolmzZvDz80NcXBwePnyIx48fo1evXvDw8MixHRMnToSOjg7++9//Ii4uDiNHjkSPHj3E74hRo0YBAFq2bImtW7fiwoULOH36NOLj4/Hs2TO4ubnhyZMnOH/+PHR1dbF+/XqF9v1iBCqyv/76S+jbt6+QlpYmJCcnC507dxbc3NyEu3fvCoIgCKGhoYKzs7MgCIIwYcIEYdCgQUJ6errw9OlTwc7OToiPjxd8fX0FX19fQRAEwcnJSYiJiRGysrKEAQMGCC9fvhQEQRD27NkjDBs2TBAEQejfv79w/vz5HHXJLufFixeCo6OjEBMTIwiCIKxbt04YNWqUEBsbK3Ts2FEQBEFITU0Vxo0bJ6Smpgr9+/cXIiMjBUEQhC1btgh///23EvdY6XHhwgVh+vTpgiAIQlZWltC/f3/Bz89PaNu2rZCUlCSkpqYKvXr1Enx9fYWXL18KnTt3FhITEwVBEIQ///xTmDx5cklWXzK+pmN03rx5woIFCwRBEISoqCihQYMGQkxMjHDo0CFh9erVgiAIQlpamtC6dWvh2rVrwvnz54X+/fuLZc6ePVsQBEE4ceKE0K1bN2XtshJz/vx5wdzcXHj06JGQlZUl9OjRQ9i8ebPQunVrISEhQRAEQZg+fbowf/58QRAEoX79+uJ79+3bJ0yYMEEQhPdttG/fPkEQBCEwMFA8Du/evSu+92P169cXAgMDBUEQhK1btwrDhg0T0tLSBBcXF+Hx48eCIAjC2bNnBTc3N0EQ3rdH9mfiY4sWLRK2bdsmCIIgODs7i5+vFStWCNu3bxfOnDkjjBo1SsjMzBSysrKEX3/9VThw4IBw+/Zt4aeffhJSU1MFQRCExYsXC6tWrRLrd/36daF9+/bCv//+W9RdW6Jy295Vq1YJ7du3F5KTk4WUlBTB2dlZ3J+5tWtaWppgZ2cnXL9+XRAEQViyZImwdevWPI+dmJgYwcnJSRAEQeFvLy8vYePGjYIgCMKjR48EBwcH4fnz54Kvr6/Qs2dPIS0tTXjx4oVgbm4uvH79Ose2TJgwQRg5cqQgl8uFmzdvCtbW1oIgCArfEYLwv++Jffv2CS1atBCSkpKE2NhYoX79+sLZs2cFQXj/GTp27Fix7uvCYg/fJ7h48SI6dOgADQ0NaGhoICgoCGlpaTh16hSCg4MRGRmJlJQUcfnu3btDXV0d3333HSwtLcXHyX1MRUUFq1atwsmTJ3H//n1cuHABKiqFO80yKioKpqamMDQ0BAD06dMH/v7+MDAwgKamJvr27QsnJyeMGTMGmpqaYk9f69at0apVKzg4OHz+jiE0bdoUurq62LFjB+7du4cHDx7AxsYGTk5O0NHRAQB06tQJb968QWRkJJ4+fYoBAwYAAORyOSpUqFCS1ZeMr+kYPXfuHBYvXgzg/XBt9nlZzs7OiIqKwubNm3Hv3j0kJibi7du3Od7/448/AgB++OEHJCYmfsru+Oo1aNAANWrUAADUrVsXSUlJcHJyEnu7+/Tpg0mTJhVYjpmZGQDAwsICS5cuRVxcHFq0aJHncLympia6du0KAOjWrRuWLl2KBw8eICYmRqGnJzk5Wfw7u0fqYy1atMCmTZtga2uLevXq4d69e3j58qXYi79t2zZERUWhe/fuAIDU1FRUq1YNSUlJePjwIXr37g0AyMjIQKNGjcRyhwwZgvbt26NOnToFbv/XJCwsLMf2Hjt2DP369YO2tjYAoH379pDL5XmWcevWLRgYGIg9qb/++qs4L7djR1dXN9dyzp8/j9mzZwMAatSoATMzM0RGRgIAbGxsoKGhgcqVK0NXVxdJSUkoX758jjIcHBwgk8lQv379Qh2HlpaW0NHREb/37ezsALx/rOybN28KfL8yMPB9AjU1xd0WGxuL0aNHw8bGBjY2NrCzs8Nvv/0mzldVVRX/lsvlOd6fLSUlBT169ECXLl3QtGlTGBkZYceOHQrLrFixAidPngQAeHl5KZT7IUEQkJmZCTU1NezZswcXLlzA2bNn0bdvX2zbtg0DBw6Ek5MTTp06hUWLFiEqKirXrmwqmhMnTsDX1xcDBgxA9+7d8erVK5QrVy7XAzwrKwuWlpZYu3YtACAtLU0hhNCn+5qOUVVVVQi5PNBo27ZtCAkJQe/evWFvb4/bt2/nulz20I9MJivk1n97PtzfMpkM5cuXVzhmsr/PPnwtk8kUpgH/21e1atXCkSNH8Pfff+PUqVPYuHEjtmzZgqFDhwIA9PX1sW7dOqioqIj7VS6XQ1VVFXK5HIaGhggKCgLw/jjNHhoG/vdw+uxjHXg/lOfp6Ynx48cjNDQU1tbWqFy5MoKDg5GRkYFq1aohKysLbm5uGDRoEADgzZs3UFVVxd69e9GhQwf4+PgAeP8Zy8rKEte3ePFijB8/Hr169UKDBg0+Zzd/Ublt79atWxXaVU1NDenp6eLrj9tVXV1docykpCSkpKTg2LFjhTp2Piz349fZ+/jDoVWZTAZBEDBlyhRER0cDgBgUczsOZTKZwv+9GRkZ4t8f1z2v75QviVfpfoKmTZvi2LFjyMjIwLt37zB48GDcuXMHo0ePRvPmzXHu3DmFA/bIkSMQBAGPHz9GVFQUrKysFMpTVVVFVlYWHjx4ABUVFQwfPhy2trY4e/asQjkAMHr0aAQFBSEoKEjhnJTsXyyxsbEAgICAANjY2OD69evo378/mjZtigkTJqBu3bq4f/8+evXqhZSUFAwcOBADBw7E9evXlbjHSo+wsDB06NABPXr0gJ6eHi5evAgAOHPmDJKTk5Geno6jR49CJpPBzMwMV69exf379wEAq1evxsKFC0uy+pLxNR2j9vb2OHToEID3PRZ37tyBTCbDuXPn0KdPH3Tu3BkymQw3b97Mt7ejtDl58qTYk7J7927Y2NgAACpWrIg7d+5AEAQxWH9s+/bt8PPzQ4cOHTBt2jQkJCSgTJkyYrtkn8z/7t07sYzAwEA0a9YMderUwevXr3Hp0iUAwL59+xR+HGRr1aqVWN7o0aOhqqoKMzMzbNu2DdbW1rC1tcXatWvFcy5tbW0RFBSElJQUZGZmYuTIkQgJCYGNjQ2OHTuGly9fQhAETJ8+HVu2bBHXY2dnh3HjxsHHx+eb+nzktr06Ojo4ffo0kpKSkJaWhmPHjonL59autWvXRkJCAu7evQsAWL9+Pf788888jx01NbUcPwKy67J3714AQExMDC5fvgxzc/M86z5nzhyxbfO7gKpixYpi3aKiovD8+fMi76cvqeQj5zeoTZs2iI6ORvfu3SGXy+Hm5oZHjx6hU6dO0NHRgbm5OVJTU8XhmdTUVPTo0QPp6emYOXNmjpPyW7RogaFDh2LdunVo2LAhOnToAC0tLTRt2hRPnjwpVJ309PQwc+ZMeHp6ir8o58yZA319fZibm8PZ2RllypRBw4YN0axZM5QpUwYTJ06EmpoaNDU1MWPGjGLfT6VRr1698NtvvyE4OBgaGhowNzdHQkICBgwYgD59+qBs2bKoWLEiNDU1UaVKFcydOxdjxoyBXC6HgYGBeCENfZ6v6RgdMWIEJk2aBBcXF9SsWRN6enrQ0tKCm5sbpk+fjo0bN0JbWxsWFhaIjY1FzZo1lbZfvhU6OjoYNmwYXF1dkZGRgcaNG4vfUePGjcPw4cOhp6cHKysrvHr1Ksf7u3btil9//RUuLi5QU1ODp6dnrsN0ABAcHIxly5ZBX18fCxYsgIaGBlasWIE5c+YgLS0NOjo6WLBgQaHq3bx5c1y8eBF169ZFlSpV8PLlS7Ro0QLA+17Amzdvonfv3sjKysKPP/6Ibt26QSaTwdPTE25ubpDL5WjYsKHYE/nh9uzbtw/btm2Dm5tbEfZkyclte93c3KCuro6ePXuifPnyqFatmrh8bu2qqamJRYsWYfz48cjIyEDNmjWxcOFCREVF5XrsNGnSBNWqVYOrqyvmzZsnlj1lyhT8/vvvCAwMBPC+105fX/+zt7Fjx44ICQlBx44d0bhxY4Wh+K+RTMivH5Q+28SJE2FtbS2ex0Clz/3793HmzBkMHDgQAODh4YFevXqhZcuWJVsxAqD8YzQoKAiGhoawsrLCkydP0L9/fxw/frzQ5+eS8nx4ZSiR1LGHj0jJqlevjmvXrsHZ2RkymQyOjo5wcnIq6WrRF1KnTh1MmzYNcrkcKioqmDlzJsMeEX1x7OEjIiIikjj+zCQiIiKSOAY+IiIiIolj4CMiIiKSOF60QUSljpGREerXr69w8YSxsTHmzJnzSeVFRUVh7969mDlzZnFVkYioWDHwEVGptGXLFlSqVKlYyrp79y7i4uKKpSwiImXgkC4R0Qf+/fdf/PLLL+jevTu6dOki3qFfLpdj9uzZ6NWrFzp27IgOHTogIiICT58+ha+vLy5duoRJkyYhPDwczs7OYnkfvvbz88PgwYPh4uIiPr1hzZo16NatG7p06YIRI0YwOBKRUrCHj4hKJTc3N4Uh3Y0bN6JChQrw8vLCwoUL0bhxYyQlJaFPnz6oV68eBEFAfHw8AgICoKKiAn9/f6xbtw5r166Fl5cXQkJCMG/ePISHh+e73sePH+Pw4cNQU1PDgQMHcPv2bezZswdqamoICAiAj4+P+OgvIqLiwsBHRKVSbkO6d+/exaNHjzB58mRxWmpqKq5fv45+/fqhQoUK2LVrF2JiYhAeHg5tbe0ir9fc3Fx8kPqpU6dw7do19OjRA8D7XsR37959xlYREeWOgY+I6P9lZWWhfPnyCAoKEqe9ePEC5cqVw+nTpzFnzhwMGjQIrVq1Qp06dXDw4MEcZchkMnx4P/uMjAyF+WXLlhX/lsvlGDJkCPr16wcASE9Px+vXr4t7s4iIeA4fEVG22rVrQ1NTUwx8T58+hbOzM6Kjo3Hu3Dk4OTmhX79+MDExwfHjx5GVlQUAUFVVRWZmJgCgUqVKePLkCV6+fAlBEHD8+PE81+fo6Ii9e/ciOTkZALBixQqMHz9eyVtJRKURe/iIiP6fhoYGVq9ejTlz5mD9+vXIzMzE6NGjYWVlBV1dXfz2229wcXGBqqoqmjRpgqNHj0Iul8PCwgLLly/HyJEjsWrVKvTt2xc9evRAlSpV0KJFizzX16tXL8TFxaF3796QyWSoWrUq5s+f/+U2mIhKDT5Ll4iIiEjiOKRLREREJHEMfEREREQSx8BHREREJHEMfEREREQSx8BHREREJHEMfEREREQSx8BHREREJHEMfEREREQS939iG1L1zV21CgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import a supervised learning model that has 'feature_importances_'\n",
    "\n",
    "# Train the supervised model on the training set \n",
    "model = AdaBoostClassifier().fit(X_train,y_train)\n",
    "\n",
    "# Extract the feature importances\n",
    "importances = model.feature_importances_\n",
    "\n",
    "# Plot\n",
    "vs.feature_plot(importances, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the five features predicted in the earlier section, 3 of them, Age, hours per week, education-num (which is a numerical label for education) are included in the list of features considered most important by Adaboost, although with different rankings.\n",
    "\n",
    "I didn't consider two important features, capital-gain and capital-loss, partly due to my lack of understanding of what they meant. After researching what they mean (profit or loss from on the sale of assets/property), it makes sense for these features to be important. People who have earned profits from sale of assets are definitely likelier to earn higher, while those who incurred losses are likely to have had lower total income.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection\n",
    "\n",
    "An interesting question here is, how does a model perform if we only use a subset of all the available features in the data? With less features required to train, the expectation is that training and prediction time is much lower — at the cost of performance metrics. From the visualization above, we see that the top five most important features contribute more than half of the importance of **all** features present in the data. This hints that we can attempt to *reduce the feature space* and simplify the information required for the model to learn. \n",
    "\n",
    "Let's see how a model that is trained only on the selected features, performs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.8680\n",
      "F-score on testing data: 0.7498\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.8423\n",
      "F-score on testing data: 0.7025\n"
     ]
    }
   ],
   "source": [
    "# Import functionality for cloning a model\n",
    "from sklearn.base import clone\n",
    "\n",
    "# Reduce the feature space\n",
    "X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "X_test_reduced = X_test[X_test.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "\n",
    "# Train on the \"best\" model found from grid search earlier\n",
    "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n",
    "\n",
    "# Make new predictions\n",
    "reduced_predictions = clf.predict(X_test_reduced)\n",
    "\n",
    "# Report scores from the final model using both versions of data\n",
    "print(\"Final Model trained on full data\\n------\")\n",
    "print(\"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5)))\n",
    "print(\"\\nFinal Model trained on reduced data\\n------\")\n",
    "print(\"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, reduced_predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, reduced_predictions, beta = 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Effects of Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On a reduced dataset, the final model's accuracy and f-score are still very comparable to the results on the full dataset. \n",
    "\n",
    "The acccuracy is ~2.7% lower, while the f-score is ~5% lower. Even though Adaboost is relatively faster than one of the other classifiers that we tried out, I'd still consider training on the reduced data (acc. to features) if training time was a factor, and we have more training points to process. This decision will also depend on how important accuracy and f-scores are (or if f-score is more important than the accuracy, as the dip for that is larger than the dip in accuracy), to make a final decision regarding this."
   ]
  }
 ],
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